Gaming Duration and Preferences: Relationships with Psychiatric Health, Gaming Addiction Scores and Academic Success in High School Students
Bibliographic record
Abstract
Problematic gaming behavior is an important problem that may lead to a recently introduced psychiatric condition named internet gaming disorder (IGD). Gaming addiction has been reported to have major influence on the lives of adolescents and young adults affected by it. Our aim was to determine relationships between gaming-related parameters, academic success, levels of depression, anxiety and stress, and gaming addiction scores. We performed a cross-sectional study comprised of 499 non-senior high school students from the Bakırköy district of Istanbul. Depression, anxiety and stress were measured with the DASS-21, gaming addiction was measured via the IGDS9-SF. A single questionnaire form was prepared to record demographics, game play behavior and preferences, DASS-21 scores and IGDS9-SF scores. Girls comprised 80.2% (n=400) of the participants in this study. Eighty-eight (17.6%) students reported that they did not play games. There was a statistically significant worsening in IGDS9 scores and all subscales of the DASS-21 with increased game playing time. Gaming addiction score was higher in those that reported being academically unsuccessful. Multivariate regression analysis revealed that the factors that increased IGDS9 scores were: time spent gaming, and preference of action, simulation or social media games. Whereas, smartphone gaming was found to be independently associated with lower IGDS9 scores. The association of higher IGDS9 scores with gaming time and preference of action, simulation and social media games, and lower scores with smartphone gaming are interesting results and may have implications in the approach to and treatment of those with IGD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".